cf_neural_average_potential_pre_spike_redux2

_.md

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config.json

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details.csv

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details2.csv

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inlet.coffee

# quick n dirty neural activity: http://en.wikipedia.org/wiki/Spike-triggered_average

svg = d3.select("svg")
svg.append("rect").attr({width:"100%", height:"100%", fill: '#C2C2C2'})

group1 = svg.append('g')
  .classed('group1', true)
  .attr
    transform: 'translate(' + [74, 367] + ')'
    
data = tributary.details
data2 = tributary.details2

 
# _".map" is the function and "data_set, (d)" are its arguments
to_numbers = (data_set) ->
  _data = _.map data_set,(d) ->
    d.op1 = +d.op1                         
    d

# converts data_set to stimuli into path format.
make_path = (data_set) ->
  xpos = 0
  path = "M"
  for val in data_set
    x_comp = " " + (xpos += 4)
    y_comp = " " + (-val.op1*10)
    path += x_comp + y_comp 
  path
    

data = to_numbers(data)
data2 = to_numbers(data2)

  
gop1 = group1.append("g").classed("op1", true)
  .style
    "stroke-width": 1.4
    "fill": "none"

# 1.0 of all spikes ,  more than 53000 spikes and their preceding 300 ms
# stimuli ( 300 ms is 150 data points at 2ms each, with sample rate 500 hz) 
# - this plot shows the average window formed from all windows preceding a spike.
gop1.append("path")
  .attr 
     d: () -> make_path(data)
  .style
    "stroke": "#232323"

# 0.01 of all spikes, every 100 spikes, but still 150 data points per window
# - this plot happens to look similar to the line gained from averaging over all
# windows, but it might be possible to select a different 1% and
# get a different plot (entirely?).
gop1.append("path")
  .attr 
     d: () -> make_path(data2)
  .style
    "stroke": "#FFFFFF"

    
# EOF #

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